Direct-injection spark-ignition engines are a promising technology to achieve high efficiency and low emissions for automotive applications. Robust operation of direct-injection spark-ignition engines requires understanding the local fuel-air mixing process. Large-eddy simulations can capture more details of the local mixing structure than traditional Reynoldsaveraged Navier-Stokes methods. Presented in this work are the results of applying a large-eddy simulation spray methodology originally developed for use with diesel injections to direct-injection spark-ignition sprays. Comparisons were carried out over a wide range of ambient temperatures (400-900 K) and densities (3-9 kg/m 3 ). To accurately simulate the large-scale vapor mixing, it was necessary to adjust spray break-up model parameters as functions of the density ratio. After this adjustment to the spray models, the large-eddy simulations matched experimental vapor penetration data and vapor images across the full range of tested ambient conditions. Liquid penetration trends with respect to changing ambient temperature were captured, but the trends with changing ambient density were not fully captured. Simulations using a Reynolds-averaged Navier-Stokes turbulence model at select conditions showed that the liquid predictions were very similar to large-eddy simulation results, but the Reynolds-averaged Navier-Stokes models were unable to accurately capture the large-scale vapor mixing and did not produce accurate vapor results.

In this work, lean mixed-mode combustion is numerically investigated using computational fluid dynamics (CFD) in a spark-ignition engine. A new E30 fuel surrogate is developed using a neural network model with matched octane numbers. A skeletal mechanism is also developed by automated mechanism reduction and by incorporating a NOx sub-mechanism. A hybrid approach that couples the G-equation model and the well-stirred reactor model is employed for turbulent combustion modeling. The developed CFD model is able to predict pressure and apparent heat release rate (AHRR) traces. Two types of combustion cycles (namely, purely-deflagrative and mixed-mode cycles) are observed. The mixed-mode cycles feature earlier flame propagation and end-gas auto-ignition, leading to two distinctive AHRR peaks. The validated CFD model is then employed to investigate the effects of NOx chemistry. The NOx chemistry is found to promote auto-ignition through residual gas, while the deflagration phase remains largely unaffected. Sensitivity analysis is finally performed to understand effects of fuel properties, including heat of vaporization (HoV) and laminar flame speed (SL). An increased HoV tends to suppress auto-ignition through charge cooling, while the impact of HoV on flame propagation is insignificant. In contrast, an increased SL is found to significantly promote both flame propagation and end-gas auto-ignition. The promoting effect of SL on auto-ignition is not a direct chemical effect; it is rather caused by an advancement of the combustion phasing, which increases compression heating of the end-gas.

The use of Large-eddy Simulations (LES) has increased due to their ability to resolve the turbulent fluctuations of engine flows and capture the resulting cycle-to-cycle variability. One drawback of LES, however, is the requirement to run multiple engine cycles to obtain the necessary cycle statistics for full validation. The standard method to obtain the cycles by running a single simulation through many engine cycles sequentially can take a long time to complete. Recently, a new strategy has been proposed by our research group to reduce the amount of time necessary to simulate the many engine cycles by running individual engine cycle simulations in parallel. With modern large computing systems this has the potential to reduce the amount of time necessary for a full set of simulated engine cycles to finish by up to an order of magnitude. In this paper, the Parallel Perturbation Methodology (PPM) is used to simulate up to 35 engine cycles of an optically accessible, pent-roof Direct-injection Spark-ignition (DISI) engine at two different motored engine operating conditions, one throttled and one un-throttled. Comparisons are made against corresponding sequential-cycle simulations to verify the similarity of results using either methodology. Mean results from the PPM approach are very similar to sequential-cycle results with less than 0.5% difference in pressure and a magnitude structure index (MSI) of 0.95. Differences in cycle-to-cycle variability (CCV) predictions are larger, but close to the statistical uncertainty in the measurement for the number of cycles simulated. PPM LES results were also compared against experimental data. Mean quantities such as pressure or mean velocities were typically matched to within 5–10%. Pressure CCVs were under-predicted, mostly due to the lack of any perturbations in the pressure boundary conditions between cycles. Velocity CCVs for the simulations had the same average magnitude as experiments, but the experimental data showed greater spatial variation in the root-mean-square (RMS). Conversely, circular standard deviation results showed greater repeatability of the flow directionality and swirl vortex positioning than the simulations.

Multicycle large-eddy simulations (LES) of motored flow in an optical engine housed at the University of Michigan have been performed. The simulated flow field is compared against particle image velocimetry (PIV) data in several cutting planes. Circular statistical methods have been used to isolate the contributions to overall turbulent fluctuations from changes in flow direction or magnitude. High levels of turbulence, as indicated by high velocity root mean square (RMS) values, exist in relatively large regions of the combustion chamber. But the circular standard deviation (CSD), a measure of the variability in flow direction independent of velocity magnitude, is much more limited to specific regions or points, indicating that much of the turbulence is from variable flow magnitude rather than variable flow direction. Using the CSD is also a promising method to identify critical points, such as vortex centers or stagnation points, within the flow, which may prove useful for future engine designers.

L arge-eddy Simulations (LES) have been carried out to investigate spray variability and its effect on cycle-tocycle flow variability in a direct-injection spark-ignition (DISI) engine under non-reacting conditions. Initial simulations were performed of an injector in a constant volume spray chamber to validate the simulation spray set-up. Comparisons showed good agreement in global spray measures such as the penetration. Local mixing data and shot-to-shot variability were also compared using Rayleigh-scattering images and probability contours. The simulations were found to reasonably match the local mixing data and shot-to-shot variability using a random-seed perturbation methodology. After validation, the same spray set-up with only minor changes was used to simulate the same injector in an optically accessible DISI engine. Particle Image Velocimetry (PIV) measurements were used to quantify the flow velocity in a horizontal plane intersecting the spark plug gap. The engine was operated in a skip-fired operating mode and comparisons focused on cycles that included fuel injection, but no spark event and therefore no combustion. 105 total LES engine cycles were simulated using a parallel cycle simulation approach and 3 different perturbation methods in an attempt to isolate the effects of shot-to-shot spray variability and the initial turbulent flow field as well as their interaction effects on overall engine CCVs. The experimental mean and standard deviations were reasonably well matched by the simulations, though quantitative comparisons near the injection event during the intake stroke were difficult due to the high uncertainty in the PIV measurements at these crank angles. The 3 simulation perturbation methods resulted in very similar results, though further analysis found the current parallel cycle approach may be limiting the ability of the simulations to isolate the spray and flow effects.